updated readme and anomaly results

FarInHeight · Feb 18, 2024 · 8da3368 · 8da3368
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diff --git a/README.md b/README.md
@@ -1,22 +1,62 @@
-# Real-Time-Anomaly-Segmentation [[Course Project](https://docs.google.com/document/d/1ElljsAprT2qX8RpePSQ3E00y_3oXrtN_CKYC6wqxyFQ/edit?usp=sharing)]
-This repository provides a starter-code setup for the Real-Time Anomaly Segmentation project of the Machine Learning Course. It consists of the code base for training ERFNet on the Cityscapes dataset and perform anomaly segmentation.
+# Real-Time Anomaly Segmentation for Road Scenes
+This repository contains the code of the __Real-Time Anomaly Segmentation for Road Scenes__ project of the __Advanced Machine Learning__ course 23/24 - Politecnico di Torino
+
+### Sample Results
+
+#### First Example
+
+* Original Image <br/>
+<img src="eval/saved_anomalies/tractor.png" alt="Tractor" style="height:256px;width:512px;width:512px;"/>
+
+* Ground Truth Anomaly <br/>
+<img src="eval/saved_anomalies/tractor_label.png" alt="Tractor Ground Truth Anomaly" style="height:256px;width:512px;"/>
+
+* Anomaly Scores <br/>
+<img src="eval/saved_anomalies/tractor_anomaly_scores.png" alt="Tractor Anomaly Scores" style="height:256px;width:512px;"/>
+
+#### Second Example
+
+* Original Image <br/>
+<img src="eval/saved_anomalies/phone_box.png" alt="Phone Box" style="height:256px;width:512px;"/>
+
+* Ground Truth Anomaly <br/>
+<img src="eval/saved_anomalies/phone_box_label.png" alt="Phone Box Truth Anomaly" style="height:256px;width:512px;"/>
+
+* Anomaly Scores <br/>
+<img src="eval/saved_anomalies/phone_box_anomaly_scores.png" alt="Phone Box Anomaly Scores" style="height:256px;width:512px;"/>
 
 ## Packages
-For instructions, please refer to the README in each folder:
+For instructions, please refer to the __README__ in each folder:
 
-* [train](train) contains tools for training the network for semantic segmentation.
-* [eval](eval) contains tools for evaluating/visualizing the network's output and performing anomaly segmentation.
-* [imagenet](imagenet) Contains script and model for pretraining ERFNet's encoder in Imagenet.
-* [trained_models](trained_models) Contains the trained models used in the papers. 
+* [train](train) contains tools for training the networks for semantic segmentation.
+* [eval](eval) contains tools for evaluating/visualizing the networks' output and performing anomaly segmentation.
+* [imagenet](imagenet) contains scripts and model for pretraining ERFNet's encoder in Imagenet.
+* [trained_models](trained_models) contains the trained models used in the papers (some networks are in the Releases section of the Repo). 
 
-## Requirements:
+## Datasets
 
 * [**The Cityscapes dataset**](https://www.cityscapes-dataset.com/): Download the "leftImg8bit" for the RGB images and the "gtFine" for the labels. **Please note that for training you should use the "_labelTrainIds" and not the "_labelIds", you can download the [cityscapes scripts](https://github.com/mcordts/cityscapesScripts) and use the [conversor](https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/createTrainIdLabelImgs.py) to generate trainIds from labelIds**
-* [**Python 3.6**](https://www.python.org/): If you don't have Python3.6 in your system, I recommend installing it with [Anaconda](https://www.anaconda.com/download/#linux)
-* [**PyTorch**](http://pytorch.org/): Make sure to install the Pytorch version for Python 3.6 with CUDA support (code only tested for CUDA 8.0). 
-* **Additional Python packages**: numpy, matplotlib, Pillow, torchvision and visdom (optional for --visualize flag)
-* **For testing the anomaly segmentation model**: Road Anomaly, Road Obstacle, and Fishyscapes dataset. All testing images are provided here [Link](https://drive.google.com/file/d/1r2eFANvSlcUjxcerjC8l6dRa0slowMpx/view).
-
-## Anomaly Inference:
-* The repo provides a pre-trained ERFNet on the cityscapes dataset that can be used to perform anomaly segmentation on test anomaly datasets.
-* Anomaly Inference Command:```python evalAnomaly.py --input='/home/shyam/ViT-Adapter/segmentation/unk-dataset/RoadAnomaly21/images/*.png```. Change the dataset path ```'/home/shyam/ViT-Adapter/segmentation/unk-dataset/RoadAnomaly21/images/*.png```accordingly.
+* **For testing the anomaly segmentation models**: All testing images are provided [here](https://drive.google.com/file/d/1r2eFANvSlcUjxcerjC8l6dRa0slowMpx/view).
+
+## Networks
+The repo provides the following pre-trained networks that can be used to perform anomaly segmentation:
+* __Erfnet__ trained on 19 classes of the Cityscapes dataset using a __Cross-Entropy loss__, __Logit Norm + Cross Entropy__, __Logit Norm + Focal Loss__, __IsoMax+ + Cross Entropy__ and __IsoMax+ + Focal Loss__
+* __BiSeNetV1__ trained on 20 classes (19 + void class) of the Cityscapes dataset
+* __Enet__ trained on 20 classes (19 + void class) of the Cityscapes dataset
+
+
+## Anomaly Inference
+To run the anomaly inferences method is possible to use the following command
+* Anomaly Inference Command: ```python evalAnomaly.py --input='/content/validation_dataset/RoadAnomaly21/images/*.png'```. Change the dataset path ```'/content/validation_dataset/RoadAnomaly21/images/*.png'``` accordingly.
+
+## Notebook
+The `AML_Project.ipynb` can be opened on Colab to run all the evaluation commands.
+
+## Authors
+
+- [Davide Sferrazza s326619](https://github.com/FarInHeight/)
+- [Davide Vitabile s330509](https://github.com/Vitabile/)
+- [Yonghu Liu s313442](https://github.com/Liu-Yonghu)
+
+## License
+[MIT License](LICENSE)
diff --git a/eval/README.md b/eval/README.md
@@ -1,6 +1,8 @@
 # Functions for evaluating/visualizing the network's output
 
-Currently there are 4 usable functions to evaluate stuff:
+Currently there are 6 usable functions to evaluate stuff:
+- evalAnomaly
+- colorized_anomly
 - eval_cityscapes_color
 - eval_cityscapes_server
 - eval_iou
@@ -12,12 +14,21 @@ This code can be used to produce anomaly segmentation results on various anomaly
 
 **Examples:**
 ```
-python evalAnomaly.py --input='/home/shyam/ViT-Adapter/segmentation/unk-dataset/RoadAnomaly21/images/*.png'
+python evalAnomaly.py --input='/content/validation_dataset/RoadAnomaly21/images/*.png'
 ```
 
 For the _MSP_ method, you can also optionally specify the temperature scaling value as:
 ```
-python evalAnomaly.py --method='msp' --temperature=2 --input='/home/shyam/ViT-Adapter/segmentation/unk-dataset/RoadAnomaly21/images/*.png'
+python evalAnomaly.py --method='msp' --temperature=2 --input='/content/validation_dataset/RoadAnomaly21/images/*.png'
+```
+## colorized_anomaly.py
+
+This code can be used to produce visual anomaly segmentation results using various method, and saving an image representing the ground truth anomaly segmentation, the resulting anomaly segmentation and a heatmap of the anomaly scores.  
+
+
+**Examples:**
+```
+python colorized_anomaly.py --input='/content/validation_dataset/RoadAnomaly21/images/*.png'
 ```
 
 ## eval_cityscapes_color.py 
@@ -28,7 +39,7 @@ This code can be used to produce segmentation of the Cityscapes images in color
 
 **Examples:**
 ```
-python eval_cityscapes_color.py --datadir /home/datasets/cityscapes/ --subset val
+python eval_cityscapes_color.py --datadir /content/cityscapes/ --subset val
 ```
 
 ## eval_cityscapes_server.py 
@@ -39,7 +50,7 @@ This code can be used to produce segmentation of the Cityscapes images and conve
 
 **Examples:**
 ```
-python eval_cityscapes_server.py --datadir /home/datasets/cityscapes/ --subset val
+python eval_cityscapes_server.py --datadir /content/cityscapes/ --subset val
 ```
 
 ## eval_iou.py 
@@ -50,7 +61,7 @@ This code can be used to calculate the IoU (mean and per-class) in a subset of i
 
 **Examples:**
 ```
-python eval_iou.py --datadir /home/datasets/cityscapes/ --subset val
+python eval_iou.py --datadir /content/cityscapes/ --subset val
 ```
 
 ## eval_forwardTime.py

diff --git a/eval/saved_anomalies/phone_box.png b/eval/saved_anomalies/phone_box.png
diff --git a/eval/saved_anomalies/phone_box_anomaly_scores.png b/eval/saved_anomalies/phone_box_anomaly_scores.png
diff --git a/eval/saved_anomalies/phone_box_label.png b/eval/saved_anomalies/phone_box_label.png
diff --git a/eval/saved_anomalies/tractor.png b/eval/saved_anomalies/tractor.png
diff --git a/eval/saved_anomalies/tractor_anomaly_scores.png b/eval/saved_anomalies/tractor_anomaly_scores.png
diff --git a/eval/saved_anomalies/tractor_label.png b/eval/saved_anomalies/tractor_label.png